OpenRaiser/Intern-Atlas huggingface dataset:
PyTOaCNN: Topology optimization using an adaptive convolutional neural network in Python
Venue: arXiv cs.CE 2024
Year: 2024

This paper introduces an adaptive convolutional neural network (CNN) architecture capable of automating various topology optimization (TO) problems with diverse underlying physics. The proposed architecture has an encoder-decoder-type structure with dense layers added at the bottleneck region to capture complex geometrical features. The network is trained using datasets obtained by the problem-specific open-source TO codes. Tensorflow and Keras are the main libraries employed to develop and to train the model. Effectiveness and robustness of the proposed adaptive CNN model are demonstrated through its performance in compliance minimization problems involving constant and design-dependent loads and in addressing bulk modulus optimization. Once trained, the model takes user's input of the volume fraction as an image and instantly generates an output image of optimized design. The proposed CNN produces high-quality results resembling those obtained via open-source TO codes with negligible performance and volume fraction errors. The paper includes complete associated Python code (Appendix A) for the proposed CNN architecture and explains each part of the code to facilitate reproducibility and ease of learning.
FedEval-LLM: Federated Evaluation of Large Language Models on Downstream Tasks with Collective Wisdom
Venue: arXiv cs.AI 2024
Year: 2024

Federated Learning (FL) has emerged as a promising solution for collaborative training of large language models (LLMs). However, the integration of LLMs into FL introduces new challenges, particularly concerning the evaluation of LLMs. Traditional evaluation methods that rely on labeled test sets and similarity-based metrics cover only a subset of the acceptable answers, thereby failing to accurately reflect the performance of LLMs on generative tasks. Meanwhile, although automatic evaluation methods that leverage advanced LLMs present potential, they face critical risks of data leakage due to the need to transmit data to external servers and suboptimal performance on downstream tasks due to the lack of domain knowledge. To address these issues, we propose a Federated Evaluation framework of Large Language Models, named FedEval-LLM, that provides reliable performance measurements of LLMs on downstream tasks without the reliance on labeled test sets and external tools, thus ensuring strong privacy-preserving capability. FedEval-LLM leverages a consortium of personalized LLMs from participants as referees to provide domain knowledge and collective evaluation capability, thus aligning to the respective downstream tasks and mitigating uncertainties and biases associated with a single referee. Experimental results demonstrate a significant improvement in the evaluation capability of personalized evaluation models on downstream tasks. When applied to FL, these evaluation models exhibit strong agreement with human preference and RougeL-score on meticulously curated test sets. FedEval-LLM effectively overcomes the limitations of traditional metrics and the reliance on external services, making it a promising framework for the evaluation of LLMs within collaborative training scenarios.
Reducing Bias in Pre-trained Models by Tuning while Penalizing Change
Venue: arXiv cs.CV 2024
Year: 2024

Deep models trained on large amounts of data often incorporate implicit biases present during training time. If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model. This behavior is especially problematic in critical areas such as autonomous driving or medical decision-making. In these scenarios, new data is often expensive and hard to come by. In this work, we present a method based on change penalization that takes a pre-trained model and adapts the weights to mitigate a previously detected bias. We achieve this by tuning a zero-initialized copy of a frozen pre-trained network. Our method needs very few, in extreme cases only a single, examples that contradict the bias to increase performance. Additionally, we propose an early stopping criterion to modify baselines and reduce overfitting. We evaluate our approach on a well-known bias in skin lesion classification and three other datasets from the domain shift literature. We find that our approach works especially well with very few images. Simple fine-tuning combined with our early stopping also leads to performance benefits for a larger number of tuning samples.
iRAG: Advancing RAG for Videos with an Incremental Approach
Venue: arXiv cs.CV 2024
Year: 2024

Retrieval-augmented generation (RAG) systems combine the strengths of language generation and information retrieval to power many real-world applications like chatbots. Use of RAG for understanding of videos is appealing but there are two critical limitations. One-time, upfront conversion of all content in large corpus of videos into text descriptions entails high processing times. Also, not all information in the rich video data is typically captured in the text descriptions. Since user queries are not known apriori, developing a system for video to text conversion and interactive querying of video data is challenging. To address these limitations, we propose an incremental RAG system called iRAG, which augments RAG with a novel incremental workflow to enable interactive querying of a large corpus of videos. Unlike traditional RAG, iRAG quickly indexes large repositories of videos, and in the incremental workflow, it uses the index to opportunistically extract more details from select portions of the videos to retrieve context relevant to an interactive user query. Such an incremental workflow avoids long video to text conversion times, and overcomes information loss issues due to conversion of video to text, by doing on-demand query-specific extraction of details in video data. This ensures high quality of responses to interactive user queries that are often not known apriori. To the best of our knowledge, iRAG is the first system to augment RAG with an incremental workflow to support efficient interactive querying of a large corpus of videos. Experimental results on real-world datasets demonstrate 23x to 25x faster video to text ingestion, while ensuring that latency and quality of responses to interactive user queries is comparable to responses from a traditional RAG where all video data is converted to text upfront before any user querying.
Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold
Venue: arXiv cs.LG 2024
Year: 2024

This paper introduces a new technique to measure the feature dependency of neural network models. The motivation is to better understand a model by querying whether it is using information from human-understandable features, e.g., anatomical shape, volume, or image texture. Our method is based on the principle that if a model is dependent on a feature, then removal of that feature should significantly harm its performance. A targeted feature is "removed" by collapsing the dimension in the data distribution that corresponds to that feature. We perform this by moving data points along the feature dimension to a baseline feature value while staying on the data manifold, as estimated by a deep generative model. Then we observe how the model's performance changes on the modified test data set, with the target feature dimension removed. We test our method on deep neural network models trained on synthetic image data with known ground truth, an Alzheimer's disease prediction task using MRI and hippocampus segmentations from the OASIS-3 dataset, and a cell nuclei classification task using the Lizard dataset.
When LLMs are Unfit Use FastFit: Fast and Effective Text Classification with Many Classes
Venue: arXiv cs.CL 2024
Year: 2024

We present FastFit, a method, and a Python package design to provide fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. FastFit utilizes a novel approach integrating batch contrastive learning and token-level similarity score. Compared to existing few-shot learning packages, such as SetFit, Transformers, or few-shot prompting of large language models via API calls, FastFit significantly improves multiclass classification performance in speed and accuracy across FewMany, our newly curated English benchmark, and Multilingual datasets. FastFit demonstrates a 3-20x improvement in training speed, completing training in just a few seconds. The FastFit package is now available on GitHub and PyPi, presenting a user-friendly solution for NLP practitioners.
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
Venue: arXiv cs.LG 2024
Year: 2024

Vertical Federated Learning (VFL) is a category of Federated Learning in which models are trained collaboratively among parties with vertically partitioned data. Typically, in a VFL scenario, the labels of the samples are kept private from all the parties except for the aggregating server, that is the label owner. Nevertheless, recent works discovered that by exploiting gradient information returned by the server to bottom models, with the knowledge of only a small set of auxiliary labels on a very limited subset of training data points, an adversary can infer the private labels. These attacks are known as label inference attacks in VFL. In our work, we propose a novel framework called KDk, that combines Knowledge Distillation and k-anonymity to provide a defense mechanism against potential label inference attacks in a VFL scenario. Through an exhaustive experimental campaign we demonstrate that by applying our approach, the performance of the analyzed label inference attacks decreases consistently, even by more than 60%, maintaining the accuracy of the whole VFL almost unaltered.
Toward a Quantum Information System Cybersecurity Taxonomy and Testbed: Exploiting a Unique Opportunity for Early Impact
Venue: arXiv cs.CR 2024
Year: 2024

Any human-designed system can potentially be exploited in ways that its designers did not envision, and information systems or networks using quantum components do not escape this reality. We are presented with a unique but quickly waning opportunity to bring cybersecurity concerns to the forefront for quantum information systems before they become widely deployed. The resources and knowledge required to do so, however, may not be common in the cybersecurity community. Yet, a nexus exist. Cybersecurity starts with risk, and there are good taxonomies for security vulnerabilities and impacts in classical systems. In this paper, we propose a preliminary taxonomy for quantum cybersecurity vulnerabilities that accounts for the latest advances in quantum information systems, and must evolve to incorporate well-established cybersecurity principles and methodologies. We envision a testbed environment designed and instrumented with the specific purpose of enabling a broad collaborative community of cybersecurity and quantum information system experts to conduct experimental evaluation of software and hardware security including both physical and virtual quantum components. Furthermore, we envision that such a resource may be available as a user facility to the open science research community.
Explainable Deep Learning Models for Dynamic and Online Malware Classification
Venue: arXiv cs.CR 2024
Year: 2024

In recent years, there has been a significant surge in malware attacks, necessitating more advanced preventive measures and remedial strategies. While several successful AI-based malware classification approaches exist categorized into static, dynamic, or online analysis, most successful AI models lack easily interpretable decisions and explanations for their processes. Our paper aims to delve into explainable malware classification across various execution environments (such as dynamic and online), thoroughly analyzing their respective strengths, weaknesses, and commonalities. To evaluate our approach, we train Feed Forward Neural Networks (FFNN) and Convolutional Neural Networks (CNN) to classify malware based on features obtained from dynamic and online analysis environments. The feature attribution for malware classification is performed by explainability tools, SHAP, LIME and Permutation Importance. We perform a detailed evaluation of the calculated global and local explanations from the experiments, discuss limitations and, ultimately, offer recommendations for achieving a balanced approach.
UIClip: A Data-driven Model for Assessing User Interface Design
Venue: arXiv cs.HC 2024
Year: 2024

User interface (UI) design is a difficult yet important task for ensuring the usability, accessibility, and aesthetic qualities of applications. In our paper, we develop a machine-learned model, UIClip, for assessing the design quality and visual relevance of a UI given its screenshot and natural language description. To train UIClip, we used a combination of automated crawling, synthetic augmentation, and human ratings to construct a large-scale dataset of UIs, collated by description and ranked by design quality. Through training on the dataset, UIClip implicitly learns properties of good and bad designs by i) assigning a numerical score that represents a UI design's relevance and quality and ii) providing design suggestions. In an evaluation that compared the outputs of UIClip and other baselines to UIs rated by 12 human designers, we found that UIClip achieved the highest agreement with ground-truth rankings. Finally, we present three example applications that demonstrate how UIClip can facilitate downstream applications that rely on instantaneous assessment of UI design quality: i) UI code generation, ii) UI design tips generation, and iii) quality-aware UI example search.
STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems
Venue: arXiv cs.AR 2024
Year: 2024

Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain higher performance by spatially distributing the parallelizable sections of an application throughout the reconfigurable device while the CPU is in charge of control-intensive sections. This work introduces an elastic Coarse-Grained Reconfigurable Architecture (CGRA) integrated into an energy-efficient RISC-V-based SoC designed for the embedded domain. The microarchitecture of CGRA supports conditionals and irregular loops, making it adaptable to domain-specific applications. Additionally, we propose specific mapping strategies that enable the efficient utilization of the CGRA for both simple applications, where the fabric is only reconfigured once (one-shot kernel), and more complex ones, where it is necessary to reconfigure the CGRA multiple times to complete them (multi-shot kernels). Large kernels also benefit from the independent memory nodes incorporated to streamline data accesses. Due to the integration of CGRA as an accelerator of the RISC-V processor enables a versatile and efficient framework, providing adaptability, processing capacity, and overall performance across various applications. The design has been implemented in TSMC 65 nm, achieving a maximum frequency of 250 MHz. It achieves a peak performance of 1.22 GOPs computing one-shot kernels and 1.17 GOPs computing multi-shot kernels. The best energy efficiency is 72.68 MOPs/mW for one-shot kernels and 115.96 MOPs/mW for multi-shot kernels. The design integrates power and clock-gating techniques to tailor the architecture to the embedded domain while maintaining performance. The best speed-ups are 17.63x and 18.61x for one-shot and multi-shot kernels. The best energy savings in the SoC are 9.05x and 11.10x for one-shot and multi-shot kernels.
Proteus: Preserving Model Confidentiality during Graph Optimizations
Venue: arXiv cs.CR 2024
Year: 2024

Deep learning (DL) models have revolutionized numerous domains, yet optimizing them for computational efficiency remains a challenging endeavor. Development of new DL models typically involves two parties: the model developers and performance optimizers. The collaboration between the parties often necessitates the model developers exposing the model architecture and computational graph to the optimizers. However, this exposure is undesirable since the model architecture is an important intellectual property, and its innovations require significant investments and expertise. During the exchange, the model is also vulnerable to adversarial attacks via model stealing. This paper presents Proteus, a novel mechanism that enables model optimization by an independent party while preserving the confidentiality of the model architecture. Proteus obfuscates the protected model by partitioning its computational graph into subgraphs and concealing each subgraph within a large pool of generated realistic subgraphs that cannot be easily distinguished from the original. We evaluate Proteus on a range of DNNs, demonstrating its efficacy in preserving confidentiality without compromising performance optimization opportunities. Proteus effectively hides the model as one alternative among up to $10^{32}$ possible model architectures, and is resilient against attacks with a learning-based adversary. We also demonstrate that heuristic based and manual approaches are ineffective in identifying the protected model. To our knowledge, Proteus is the first work that tackles the challenge of model confidentiality during performance optimization. Proteus will be open-sourced for direct use and experimentation, with easy integration with compilers such as ONNXRuntime.
Latent Concept-based Explanation of NLP Models
Venue: arXiv cs.CL 2024
Year: 2024

Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts aimed at explaining these predictions rely on input features, specifically, the words within NLP models. However, such explanations are often less informative due to the discrete nature of these words and their lack of contextual verbosity. To address this limitation, we introduce the Latent Concept Attribution method (LACOAT), which generates explanations for predictions based on latent concepts. Our foundational intuition is that a word can exhibit multiple facets, contingent upon the context in which it is used. Therefore, given a word in context, the latent space derived from our training process reflects a specific facet of that word. LACOAT functions by mapping the representations of salient input words into the training latent space, allowing it to provide latent context-based explanations of the prediction.
Sociotechnical Considerations for SLAM Anchors in Location-Based AR
Venue: arXiv cs.HC 2024
Year: 2024

In this position paper, we explore the power of storytelling and its connection to place through the use of Augmented Reality (AR) technology, particularly within the context of Thámien Ohlone history on the Santa Clara University campus. To do this, we utilized SLAM and 8th Wall to create virtual, location-based experiences that geolocate tribal stories at present-day sites, showcase the living culture of the Thámien Ohlone tribe, and advocate for physical markers that could exist to recognize their story. When doing so, we made sure to select locations that added to the story each stop tells to serve as our anchors. Our research then investigates both the social and technical considerations involved in selecting anchors for AR experiences, using the Thámien Ohlone AR Tour as a case study.
Just Like Me: The Role of Opinions and Personal Experiences in The Perception of Explanations in Subjective Decision-Making
Venue: arXiv cs.HC 2024
Year: 2024

As large language models (LLMs) advance to produce human-like arguments in some contexts, the number of settings applicable for human-AI collaboration broadens. Specifically, we focus on subjective decision-making, where a decision is contextual, open to interpretation, and based on one's beliefs and values. In such cases, having multiple arguments and perspectives might be particularly useful for the decision-maker. Using subtle sexism online as an understudied application of subjective decision-making, we suggest that LLM output could effectively provide diverse argumentation to enrich subjective human decision-making. To evaluate the applicability of this case, we conducted an interview study (N=20) where participants evaluated the perceived authorship, relevance, convincingness, and trustworthiness of human and AI-generated explanation-text, generated in response to instances of subtle sexism from the internet. In this workshop paper, we focus on one troubling trend in our results related to opinions and experiences displayed in LLM argumentation. We found that participants rated explanations that contained these characteristics as more convincing and trustworthy, particularly so when those opinions and experiences aligned with their own opinions and experiences. We describe our findings, discuss the troubling role that confirmation bias plays, and bring attention to the ethical challenges surrounding the AI generation of human-like experiences.
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs
Venue: arXiv cs.AI 2024
Year: 2024

The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research suggests an approach based on reinforcement learning (RL), specifically utilizing Deep Q Networks (DQN) to enhance the process of integrating contexts into knowledge graphs. By considering the state of the knowledge graph as environment states defining actions as operations for integrating contexts and using a reward function to gauge the improvement in knowledge graph quality post-integration, this method aims to automatically develop strategies for optimal context integration. Our DQN model utilizes networks as function approximators, continually updating Q values to estimate the action value function, thus enabling effective integration of intricate and dynamic context information. Initial experimental findings show that our RL method outperforms techniques in achieving precise context integration across various standard knowledge graph datasets, highlighting the potential and effectiveness of reinforcement learning in enhancing and managing knowledge graphs.
Cross-Modal Adapter: Parameter-Efficient Transfer Learning Approach for Vision-Language Models
Venue: arXiv cs.CV 2024
Year: 2024

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overlook the text modality's importance and cross-modal cues for the efficient adaptation of parameters in visual-language models. This work introduces a cross-modal parameter-efficient approach named XMAdapter. XMAdapter establishes cache models for both text and image modalities. It then leverages retrieval through visual-language bimodal information to gather clues for inference. By dynamically adjusting the affinity ratio, it achieves cross-modal fusion, decoupling different modal similarities to assess their respective contributions. Additionally, it explores hard samples based on differences in cross-modal affinity and enhances model performance through adaptive adjustment of sample learning intensity. Extensive experimental results on benchmark datasets demonstrate that XMAdapter outperforms previous adapter-based methods significantly regarding accuracy, generalization, and efficiency.